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        <h3>
            第1章 欢迎来到 Python3 玩转机器学习
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                <ul>
                        <li class='finished'>
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                                <a target="_blank" href="/lesson/169.html#mid=9690">
                    <span class="title_info">1-1  什么是机器学习 </span> (20:14)                                                                            </a>


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                    <span class="title_info">1-2 课程涵盖的内容和理念 </span> (13:25)                                                                            </a>


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                    <span class="title_info">1-3 课程所使用的主要技术栈 </span> (14:10)                                                                            </a>


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        <h3>
            第2章 机器学习基础
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                    <span class="title_info">2-1 机器学习世界的数据 </span> (18:27)                                                                            </a>


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                    <span class="title_info">2-2 机器学习的主要任务 </span> (16:00)                                                                            </a>


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                    <span class="title_info">2-3  监督学习，非监督学习，半监督学习和增强学习 </span> (17:38)                                                                            </a>


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                    <span class="title_info">2-4 批量学习，在线学习，参数学习和非参数学习 </span> (11:53)                                                                            </a>


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                    <span class="title_info">2-5 和机器学习相关的“哲学”思考 </span> (12:00)                                                                            </a>


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                    <span class="title_info">2-6 课程使用环境搭建 </span> (13:08)                                                                            </a>


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        <h3>
            第3章 Jupyter Notebook, numpy和matplotlib
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                    <span class="title_info">3-1 Jupyter Notebook基础 </span> (18:42)                                                                            </a>


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                    <span class="title_info">3-2 Jupyter Notebook中的魔法命令 </span> (20:51)                                                                            </a>


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                    <span class="title_info">3-3 Numpy数据基础 </span> (11:06)                                                                            </a>


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                    <span class="title_info">3-4 创建Numpy数组(和矩阵) </span> (19:16)                                                                            </a>


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                    <span class="title_info">3-5 Numpy数组(和矩阵)的基本操作 </span> (18:15)                                                                            </a>


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                    <span class="title_info">3-6 Numpy数组(和矩阵)的合并与分割 </span> (18:00)                                                                            </a>


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                    <span class="title_info">3-7 Numpy中的矩阵运算 </span> (23:11)                                                                            </a>


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                    <span class="title_info">3-8 Numpy中的聚合运算 </span> (13:19)                                                                            </a>


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                    <span class="title_info">3-9 Numpy中的arg运算 </span> (11:12)                                                                            </a>


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                    <span class="title_info">3-10 Numpy中的比较和Fancy Indexing </span> (22:36)                                                                            </a>


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                    <span class="title_info">3-11 Matplotlib数据可视化基础 </span> (18:05)                                                                            </a>


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                    <span class="title_info">3-12 数据加载和简单的数据探索 </span> (12:49)                                                                            </a>


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        <h3>
            第4章  最基础的分类算法-k近邻算法 kNN
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                <ul>
                        <li class='finished'>
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                                <a target="_blank" href="/lesson/169.html#mid=9843">
                    <span class="title_info">4-1 k近邻算法基础 </span> (22:42)                                                                            </a>


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                    <span class="title_info">4-2 scikit-learn中的机器学习算法封装 </span> (22:22)                                                                            </a>


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                    <span class="title_info">4-3 训练数据集，测试数据集 </span> (22:46)                                                                            </a>


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                    <span class="title_info">4-4 分类准确度 </span> (19:20)                                                                            </a>


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                    <span class="title_info">4-5 超参数 </span> (21:36)                                                                            </a>


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                    <span class="title_info">4-6 网格搜索与k近邻算法中更多超参数 </span> (17:24)                                                                            </a>


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                    <span class="title_info">4-7 数据归一化 </span> (15:27)                                                                            </a>


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                    <span class="title_info">4-8 scikit-learn中的Scaler </span> (19:24)                                                                            </a>


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                    <span class="title_info">4-9 更多有关k近邻算法的思考 </span> (10:22)                                                                            </a>


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        <h3>
            第5章 线性回归法
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                <ul>
                        <li class=''>
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                    <span class="title_info">5-1 简单线性回归 </span> (18:06)                                                                            </a>


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                    <span class="title_info">5-2 最小二乘法 </span> (11:27)                                                                            </a>


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                    <span class="title_info">5-3 简单线性回归的实现 </span> (14:09)                                                                            </a>


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                    <span class="title_info">5-4 向量化 </span> (12:02)                                                                            </a>


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                    <span class="title_info">5-5 衡量线性回归法的指标：MSE，RMSE和MAE </span> (22:45)                                                                            </a>


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                    <span class="title_info">5-6 最好的衡量线性回归法的指标：R Squared </span> (16:28)                                                                            </a>


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                    <span class="title_info">5-7 多元线性回归和正规方程解 </span> (15:58)                                                                            </a>


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                    <span class="title_info">5-8 实现多元线性回归 </span> (13:08)                                                                            </a>


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                    <span class="title_info">5-9 使用scikit-learn解决回归问题 </span> (12:42)                                                                            </a>


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                    <span class="title_info">5-10 线性回归的可解释性和更多思考 </span> (11:53)                                                                            </a>


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            第6章  梯度下降法
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                    <span class="title_info">6-1 什么是梯度下降法 </span> (16:20)                                                                            </a>


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                    <span class="title_info">6-2 模拟实现梯度下降法 </span> (20:11)                                                                            </a>


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                    <span class="title_info">6-3 线性回归中的梯度下降法 </span> (10:56)                                                                            </a>


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                    <span class="title_info">6-4 实现线性回归中的梯度下降法 </span> (14:06)                                                                            </a>


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                    <span class="title_info">6-5 梯度下降法的向量化和数据标准化 </span> (22:14)                                                                            </a>


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                    <span class="title_info">6-6 随机梯度下降法 </span> (17:34)                                                                            </a>


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                    <span class="title_info">6-7  scikit-learn中的随机梯度下降法 </span> (15:40)                                                                            </a>


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                    <span class="title_info">6-8  如何确定梯度计算的准确性？调试梯度下降法 </span> (16:29)                                                                            </a>


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                    <span class="title_info">6-9 有关梯度下降法的更多深入讨论 </span> (08:37)                                                                            </a>


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        <h3>
            第7章  PCA与梯度上升法
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                    <span class="title_info">7-1 什么是PCA </span> (17:45)                                                                            </a>


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                    <span class="title_info">7-2 使用梯度上升法求解PCA问题 </span> (09:10)                                                                            </a>


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                    <span class="title_info">7-3 求数据的主成分PCA </span> (20:09)                                                                            </a>


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                    <span class="title_info">7-4 求数据的前n个主成分 </span> (17:36)                                                                            </a>


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                    <span class="title_info">7-5 高维数据映射为低维数据 </span> (19:29)                                                                            </a>


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                    <span class="title_info">7-6 scikit-learn中的PCA </span> (18:57)                                                                            </a>


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                    <span class="title_info">7-7 试手MNIST数据集 </span> (12:06)                                                                            </a>


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                    <span class="title_info">7-8 关于 MNIST 数据集的最新获得方式 </span>                                                                             </a>


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                    <span class="title_info">7-9 使用PCA对数据进行降噪 </span> (10:00)                                                                            </a>


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                    <span class="title_info">7-10 人脸识别与特征脸 </span> (13:57)                                                                            </a>


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        <h3>
            第8章  多项式回归与模型泛化
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                    <span class="title_info">8-1 什么是多项式回归 </span> (11:38)                                                                            </a>


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                    <span class="title_info">8-2  scikit-learn中的多项式回归与Pipeline </span> (16:26)                                                                            </a>


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                    <span class="title_info">8-3 过拟合与欠拟合 </span> (14:22)                                                                            </a>


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                    <span class="title_info">8-4 为什么要有训练数据集与测试数据集 </span> (16:09)                                                                            </a>


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                    <span class="title_info">8-5  学习曲线 </span> (15:28)                                                                            </a>


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                    <span class="title_info">8-6  验证数据集与交叉验证 </span> (25:20)                                                                            </a>


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                    <span class="title_info">8-7 偏差方差平衡 </span> (15:16)                                                                            </a>


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                    <span class="title_info">8-8  模型泛化与岭回归  </span> (19:15)                                                                            </a>


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                    <span class="title_info">8-9  LASSO </span> (16:59)                                                                            </a>


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                    <span class="title_info">8-10  L1, L2和弹性网络 </span> (14:25)                                                                            </a>


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        <h3>
            第9章  逻辑回归
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                    <span class="title_info">9-1 什么是逻辑回归 </span> (16:07)                                                                            </a>


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                    <span class="title_info">9-2 逻辑回归的损失函数 </span> (15:00)                                                                            </a>


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                    <span class="title_info">9-3  逻辑回归损失函数的梯度 </span> (17:50)                                                                            </a>


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                    <span class="title_info">9-4 实现逻辑回归算法 </span> (14:29)                                                                            </a>


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                    <span class="title_info">9-5 决策边界 </span> (21:09)                                                                            </a>


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                    <span class="title_info">9-6 在逻辑回归中使用多项式特征 </span> (15:09)                                                                            </a>


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                    <span class="title_info">9-7 scikit-learn中的逻辑回归 </span> (17:22)                                                                            </a>


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                    <span class="title_info">9-8  OvR与OvO </span> (19:09)                                                                            </a>


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        <h3>
            第10章 评价分类结果
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                    <span class="title_info">10-1 准确度的陷阱和混淆矩阵 </span> (15:06)                                                                            </a>


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                    <span class="title_info">10-2 精准率和召回率 </span> (11:51)                                                                            </a>


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                    <span class="title_info">10-3 实现混淆矩阵，精准率和召回率 </span> (15:42)                                                                            </a>


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                    <span class="title_info">10-4 F1 Score </span> (17:42)                                                                            </a>


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                    <span class="title_info">10-5 精准率和召回率的平衡 </span> (20:18)                                                                            </a>


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                    <span class="title_info">10-6 精准率-召回率曲线 </span> (14:21)                                                                            </a>


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                    <span class="title_info">10-7 ROC曲线 </span> (16:52)                                                                            </a>


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                    <span class="title_info">10-8 多分类问题中的混淆矩阵 </span> (13:30)                                                                            </a>


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            第11章 支撑向量机 SVM
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                    <span class="title_info">11-1 什么是SVM </span> (13:54)                                                                            </a>


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                    <span class="title_info">11-2 SVM背后的最优化问题 </span> (19:21)                                                                            </a>


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                    <span class="title_info">11-3 Soft Margin SVM </span> (16:12)                                                                            </a>


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                    <span class="title_info">11-4 scikit-learn中的SVM </span> (21:23)                                                                            </a>


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                    <span class="title_info">11-5 SVM中使用多项式特征和核函数 </span> (12:43)                                                                            </a>


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                    <span class="title_info">11-6 到底什么是核函数 </span> (15:31)                                                                            </a>


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                    <span class="title_info">11-7 RBF核函数 </span> (20:46)                                                                            </a>


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                    <span class="title_info">11-8 RBF核函数中的gamma </span> (13:39)                                                                            </a>


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                    <span class="title_info">11-9 SVM思想解决回归问题 </span> (10:46)                                                                            </a>


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            第12章 决策树
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                    <span class="title_info">12-1 什么是决策树 </span> (12:01)                                                                            </a>


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                    <span class="title_info">12-2 信息熵 </span> (16:30)                                                                            </a>


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                    <span class="title_info">12-3 使用信息熵寻找最优划分 </span> (20:20)                                                                            </a>


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